Finance Manager

Fenchurch Street
9 months ago
Applications closed

Related Jobs

View all jobs

Finance Master Data Lead: Data Quality & Governance

Data Science Graduate

Data Science Consultant - Health

Senior Business Intelligence Finance and Commercial Manager

Data Governance Manager

Data Governance Manager

Finance Manager – (Systems & Control) EC3A 3BP — Hybrid working, 4-Month Contract Sector: Construction / Infrastructure / Finance Rate: £350 – £450 per day (Inside IR35)

An excellent opportunity has opened up for a Finance Manager  (Systems & Control) to join a FTSE-listed infrastructure business initially on a 4-month contract.

This is a key position within the central finance team, responsible for overseeing finance systems, maintaining data integrity, and supporting key reporting platforms — with direct exposure to senior stakeholders.

What You’ll Be Doing
As Finance Manager, you’ll play a lead role in managing the organisation’s budgeting and reporting system (Mercur) and internal timesheet platform. Your duties will include:

Acting as the primary contact for users of the budgeting and timesheet systems, ensuring queries are resolved promptly and accurately.
Managing and developing a Finance Assistant, supporting their day-to-day workload and professional growth.
Overseeing daily, weekly and monthly financial systems administration, ensuring data flows are accurate and reports are reliable.
Monitoring exception reports and coordinating data cleansing activities to maintain consistent and usable information.
Defining and documenting best practices, standardising procedures and maintaining compliance with finance policies.
Performing system access audits to ensure controls are in place and adhered to.
Supporting the Senior Finance Manager with the administration of the wider enterprise resource planning (ERP) system (Oracle), deputising as needed.
Contributing to finance system improvement projects and driving process enhancement across the function.
To be successful as a Finance Manager, you will bring:

Prior experience in a finance systems role, ideally with exposure to reporting and budgeting tools.
Strong working knowledge of financial controls, data governance and system access policies.
Experience with enterprise systems such as Oracle, mercur  or similar ERP platforms.
Experience working within a company that focusses on contractors and temporary workers
An organised and analytical mindset, with excellent attention to detail and problem-solving skills.
Strong communication skills and the ability to work confidently with a range of stakeholders.
Experience mentoring or managing junior team members in a structured environment. Why Apply?
This Finance Manager position offers a unique opportunity to shape how financial data is captured, controlled and utilised across a large, complex organisation. You’ll work in a hybrid setup, reporting to senior finance leaders, and gain exposure across both project and central finance functions. It’s a high-impact contract role with real visibility and the potential to extend or lead to longer-term opportunities.

Interested in this 4-month contract?
Apply now to secure your next opportunity as a Finance Manager – Systems & Control

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.